Adaptive margin slack minimization in RKHS for classification
Paper i proceeding, 2016
In this paper, we design a novel regularized empirical risk minimization technique for classification called Adaptive Margin Slack Minimization (AMSM). The proposed method is based on minimizing a regularized upper bound of the misclassification error. Compared to the cost function of the classical L2-SVM, AMSM can be interpreted as minimizing a tighter bound with some additional flexibilities regarding the choice of marginal hyperplane. A hyperparameter-free adaptive algorithm is presented for finding a solution to the proposed risk function. Numerical results shows that AMSM outperforms L2-SVM on the tested standard datasets.
Structural Risk Minimization
L2-SVM
Adaptive Margin
Reproducing Kernel Hilbert Space
Författare
Yinan Yu
Chalmers, Signaler och system, Signalbehandling och medicinsk teknik
Konstantinos I. Diamantaras
TEI of Thessaloniki
Chalmers, Signaler och system, Signalbehandling och medicinsk teknik
Princeton University
Tomas McKelvey
Chalmers, Signaler och system, Signalbehandling och medicinsk teknik
S. Y. Kung
TEI of Thessaloniki
Chalmers, Signaler och system, Signalbehandling och medicinsk teknik
Princeton University
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
15206149 (ISSN)
Vol. 2016-May 2319-2323978-1-4799-9988-0 (ISBN)
Ämneskategorier
Signalbehandling
DOI
10.1109/ICASSP.2016.7472091
ISBN
978-1-4799-9988-0